Discrimination in lending can occur either in face-to-face decisions or in algorithmic scoring.We provide a workable interpretation of the courts' legitimate-business-necessity defense of statistical discrimination.We then estimate the extent of racial/ethnic discrimination in the largest consumer-lending market using an identification afforded by the pricing of mortgage credit risk by Fannie Mae and Freddie Mac.We find that lenders charge Latinx/African-American borrowers 7.9 and 3.6 basis points more for purchase and refinance mortgages respectively, costing them $765M in aggregate per year in extra interest.FinTech algorithms also discriminate, but 40% less than face-to-face lenders.These results are consistent with both FinTech and non-FinTech lenders extracting monopoly rents in weaker competitive environments or profiling borrowers on low-shopping behavior.Such strategic pricing is not illegal per se, but under the law, it cannot result in discrimination.The lower levels of price discrimination by algorithms suggests that removing face-to-face interactions can reduce discrimination.Further silver linings emerge in the FinTech era: (1) Discrimination is declining; algorithmic lending may have increased competition or encouraged more shopping with the ease of platform applications.(2) We find that 0.74-1.3 million minority applications were rejected between 2009 and 2015 due to discrimination; however, FinTechs do not discriminate in loan approval.
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